We
sincerely appreciate your interests on our research group lead by
Prof. Ying-Cheng Lai from the School of Electrical, Computer and Energy
Engineering, Arizona State University.

The abstract reads:

Given
a complex geospatial network with nodes distributed in a
two-dimensional region of physical space, can the locations of the nodes
be determined and their connection patterns be uncovered based solely
on data? We consider the realistic situation where time series/signals
can be collected from a single location. A key challenge is that the
signals collected are necessarily time delayed, due to the varying
physical distances from the nodes to the data collection centre. To meet
this challenge, we develop a compressive-sensing-based approach
enabling reconstruction of the full topology of the underlying
geospatial network and more importantly, accurate estimate of the time
delays. A standard triangularization algorithm can then be employed to
find the physical locations of the nodes in the network. We further
demonstrate successful detection of a hidden node (or a hidden source or
threat), from which no signal can be obtained, through accurate
detection of all its neighboring nodes. As a geospatial network has the
feature that a node tends to connect with geophysically nearby nodes,
the localized region that contains the hidden node can be identified.

And it can be highlighted by its Fig. 2:

It
shows that, solely using time series collected from spatially
distributed objects, we can accurately reconstruct their connection
pattern, nodal dynamics and spatial locations. We can then locate the
hidden node by identifying all its neighboring nodes with abnormal
reconstructed results from compressive sensing (Fig. 7):